Positive Technology Journal

Brain-machine interfaces: past, present and future.

Trends Neurosci. 2006 Sep;29(9):536-46

Authors: Lebedev MA, Nicolelis MA

Since the original demonstration that electrical activity generated by ensembles of cortical neurons can be employed directly to control a robotic manipulator, research on brain-machine interfaces (BMIs) has experienced an impressive growth. Today BMIs designed for both experimental and clinical studies can translate raw neuronal signals into motor commands that reproduce arm reaching and hand grasping movements in artificial actuators. Clearly, these developments hold promise for the restoration of limb mobility in paralyzed subjects. However, as we review here, before this goal can be reached several bottlenecks have to be passed. These include designing a fully implantable biocompatible recording device, further developing real-time computational algorithms, introducing a method for providing the brain with sensory feedback from the actuators, and designing and building artificial prostheses that can be controlled directly by brain-derived signals. By reaching these milestones, future BMIs will be able to drive and control revolutionary prostheses that feel and act like the human arm.

Via Networked Performance

Brain Waves Drawing: Live Performance by Hideki Nakazawa: Nov 4-5, 2006 at Fuchu Art Museum, Tokyo Supported by Nihon Kohden.

Not to Draw by Hand. To Draw by Brain: Artists usually draw pictures by hand with brushes or pencils. However, the activities of brains must be more important and essential than the ones of hands at the moment of creating art. Therefore, I decided to draw pictures with electrodes being set on my head through controlling the activities of my own brain. The curved lines so-called "brain waves" in medicine must be the "drawings" in the world of fine art, directly drawn by my brain without using hands.

Electro-encephalogram based brain-computer interface: improved performance by mental practice and concentration skills.

Med Biol Eng Comput. 2006 Oct 7;

Authors: Mahmoudi B, Erfanian A

Mental imagination is the essential part of the most EEG-based communication systems. Thus, the quality of mental rehearsal, the degree of imagined effort, and mind controllability should have a major effect on the performance of electro-encephalogram (EEG) based brain-computer interface (BCI). It is now well established that mental practice using motor imagery improves motor skills. The effects of mental practice on motor skill learning are the result of practice on central motor programming. According to this view, it seems logical that mental practice should modify the neuronal activity in the primary sensorimotor areas and consequently change the performance of EEG-based BCI. For developing a practical BCI system, recognizing the resting state with eyes opened and the imagined voluntary movement is important. For this purpose, the mind should be able to focus on a single goal for a period of time, without deviation to another context. In this work, we are going to examine the role of mental practice and concentration skills on the EEG control during imaginative hand movements. The results show that the mental practice and concentration can generally improve the classification accuracy of the EEG patterns. It is found that mental training has a significant effect on the classification accuracy over the primary motor cortex and frontal area.

Brain-computer interfaces for control of neuroprostheses: from synchronous to asynchronous mode of operation.

Biomed Tech (Berl). 2006;51(2):57-63

Authors: Müller-Putz GR, Scherer R, Pfurtscheller G, Rupp R

Transferring a brain-computer interface (BCI) from the laboratory environment into real world applications is directly related to the problem of identifying user intentions from brain signals without any additional information in real time. From the perspective of signal processing, the BCI has to have an uncued or asynchronous design. Based on the results of two clinical applications, where 'thought' control of neuroprostheses based on movement imagery in tetraplegic patients with a high spinal cord injury has been established, the general steps from a synchronous or cue-guided BCI to an internally driven asynchronous brain-switch are discussed. The future potential of BCI methods for various control purposes, especially for functional rehabilitation of tetraplegics using neuroprosthetics, is outlined.

Via Mind Hacks 

A team of three Italian researchers (Emanuele Olivetti, Diego Sona, and Sriharsha Veeramachaneni) won $10000 in a brain-activity interpretation competition. Entrants were provided with the fMRI data and behavioural reports recorded when four people watched two movies. The competitors' task was to create an algorithm that could use the viewers ongoing brain activity to predict what they were thinking and feeling as the film unfolded.

The Italian team resulted to be the most accurate, with a correlation of .86 for basic features, such as whether an instant of the film contained music. The full results are here.

A P300 event-related potential brain-computer interface (BCI): The effects of matrix size and inter stimulus interval on performance.

Biol Psychol. 2006 Jul 21;

Authors: Sellers EW, Krusienski DJ, McFarland DJ, Vaughan TM, Wolpaw JR

We describe a study designed to assess properties of a P300 brain-computer interface (BCI). The BCI presents the user with a matrix containing letters and numbers. The user attends to a character to be communicated and the rows and columns of the matrix briefly intensify. Each time the attended character is intensified it serves as a rare event in an oddball sequence and it elicits a P300 response. The BCI works by detecting which character elicited a P300 response. We manipulated the size of the character matrix (either 3x3 or 6x6) and the duration of the inter stimulus interval (ISI) between intensifications (either 175 or 350ms). Online accuracy was highest for the 3x3 matrix 175-ms ISI condition, while bit rate was highest for the 6x6 matrix 175-ms ISI condition. Average accuracy in the best condition for each subject was 88%. P300 amplitude was significantly greater for the attended stimulus and for the 6x6 matrix. This work demonstrates that matrix size and ISI are important variables to consider when optimizing a BCI system for individual users and that a P300-BCI can be used for effective communication.

Nature 442, 164-171(13 July 2006)

Leigh R. Hochberg, Mijail D. Serruya, Gerhard M. Friehs, Jon A. Mukand, Maryam Saleh, Abraham H. Caplan, Almut Branner, David Chen, Richard D. Penn and John P. Donoghue

Neuromotor prostheses (NMPs) aim to replace or restore lost motor functions in paralysed humans by routeing movement-related signals from the brain, around damaged parts of the nervous system, to external effectors. To translate preclinical results from intact animals to a clinically useful NMP, movement signals must persist in cortex after spinal cord injury and be engaged by movement intent when sensory inputs and limb movement are long absent. Furthermore, NMPs would require that intention-driven neuronal activity be converted into a control signal that enables useful tasks. Here we show initial results for a tetraplegic human (MN) using a pilot NMP. Neuronal ensemble activity recorded through a 96-microelectrode array implanted in primary motor cortex demonstrated that intended hand motion modulates cortical spiking patterns three years after spinal cord injury. Decoders were created, providing a 'neural cursor' with which MN opened simulated e-mail and operated devices such as a television, even while conversing. Furthermore, MN used neural control to open and close a prosthetic hand, and perform rudimentary actions with a multi-jointed robotic arm. These early results suggest that NMPs based upon intracortical neuronal ensemble spiking activity could provide a valuable new neurotechnology to restore independence for humans with paralysis.

A high-performance brain-computer interface.

Nature. 2006 Jul 13;442(7099):195-8

Authors: Santhanam G, Ryu SI, Yu BM, Afshar A, Shenoy KV

Recent studies have demonstrated that monkeys and humans can use signals from the brain to guide computer cursors. Brain-computer interfaces (BCIs) may one day assist patients suffering from neurological injury or disease, but relatively low system performance remains a major obstacle. In fact, the speed and accuracy with which keys can be selected using BCIs is still far lower than for systems relying on eye movements. This is true whether BCIs use recordings from populations of individual neurons using invasive electrode techniques or electroencephalogram recordings using less- or non-invasive techniques. Here we present the design and demonstration, using electrode arrays implanted in monkey dorsal premotor cortex, of a manyfold higher performance BCI than previously reported. These results indicate that a fast and accurate key selection system, capable of operating with a range of keyboard sizes, is possible (up to 6.5 bits per second, or approximately 15 words per minute, with 96 electrodes). The highest information throughput is achieved with unprecedentedly brief neural recordings, even as recording quality degrades over time. These performance results and their implications for system design should substantially increase the clinical viability of BCIs in humans.

Re-blogged from KurzweilAI.net

Via KurzweilAI.net 

Researchers at Columbia University are combining the processing power of the human brain with computer vision to develop a novel device that will allow people to search through images ten times faster than they can on their own. 

The "cortically coupled computer vision system," known as C3 Vision, is the brainchild of professor Paul Sajda, director of the Laboratory for Intelligent Imaging and Neural Computing at Columbia University. He received a one-year, $758,000 grant from Darpa for the project in late 2005.

The brain emits a signal as soon as it sees something interesting, and that "aha" signal can be detected by an electroencephalogram, or EEG cap. While users sift through streaming images or video footage, the technology tags the images that elicit a signal, and ranks them in order of the strength of the neural signatures. Afterwards, the user can examine only the information that their brains identified as important, instead of wading through thousands of images.

Read the full story on Wired 

In a study published in the journal Nature this week, researchers from Boston-based Cyberkinetics Neurotechnology Systems describe how two paralyzed patients with a surgically implanted neural device successfully controlled a computer and, in one case, a robotic arm, using only their thoughts. 

These findings include the ability to voluntarily generate signals in the dorsal pre-motor cortex, the area of the brain responsible for the planning, selection and execution of movement. While accuracy levels have been previously published, the current study reveals unprecedented speed in retrieving and interpreting the neural signals that can be applied to the operation of external devices that require fast, accurate selections, such as typing.

The brain-computer interface used in the study consists of an internal sensor to detect brain cell activity and external processors that convert these brain signals into a computer-mediated output under the person's own control.

According to John Donoghue, Chief Scientific Officer of Cyberkinetics, and a co-inventor of the BrainGate technology, "The results achieved from this study demonstrate the utility and versatility of Cyberkinetics' neural sensing technology to achieve very rapid, accurate decoding - about as fast as humans ordinarily make decisions to move when asked. The contributions of complementary research with our electrode and data acquisition technology should enhance our development of the BrainGate System in its ability to, one day, enable those with severe paralysis or other neurological conditions to lead more independent lives."

See video here 

From The Observer

A new 'virtual helmet' which harnesses the power of brain waves is allowing severely disabled people to feel as if they can walk and move again, opening up the prospect of using the mind to help them control wheelchairs, computers and even false limbs.

Just by imagining their feet moving, patients using wheelchairs can again experience what it feels like to stroll down a high street, thanks to the work of British scientists who have found a new way of using the power of thought. They have devised the helmet which can link brain wave patterns to a virtual reality system, allowing the wearer to enter an illusory world of movement.

The new system has been tried out for the first time by an Austrian man who became a paraplegic after a swimming accident. Tom Schweiger was injured on holiday in Greece seven years ago when a huge wave swept him on to rocks, severing the spinal cord in his neck and leaving him paralysed apart from some movement in his left arm.

Last week 31-year-old Schweiger was able to enter a different virtual world when the scientists from his home country and a team at University College London tested the new system. When he was asked by researchers to think about moving either his foot or his hand, the changes in his brain waves - or electroencephalogram (EEG) signals - were recorded by electrodes on the top of his head. These were then turned into a control signal which was linked up to the virtual reality system.

Schweiger was given special 3D glasses to wear so that the images created in the 'virtual cave' created for the experiment, made up of a four-sided room complete with stereo sound and projected images, gave him the illusion of walking through a street. Different characters appeared on the screen and talked to him and he was asked to respond...

 

Read the full article 

Decoding the visual and subjective contents of the human brain 

Yukiyasu Kamitani & Frank Tong

Nature Neuroscience  8, 679 - 685 (2005) 
 

The potential for human neuroimaging to read out the detailed contents of a person's mental state has yet to be fully explored. We investigated whether the perception of edge orientation, a fundamental visual feature, can be decoded from human brain activity measured with functional magnetic resonance imaging (fMRI). Using statistical algorithms to classify brain states, we found that ensemble fMRI signals in early visual areas could reliably predict on individual trials which of eight stimulus orientations the subject was seeing. Moreover, when subjects had to attend to one of two overlapping orthogonal gratings, feature-based attention strongly biased ensemble activity toward the attended orientation. These results demonstrate that fMRI activity patterns in early visual areas, including primary visual cortex (V1), contain detailed orientation information that can reliably predict subjective perception. Our approach provides a framework for the readout of fine-tuned representations in the human brain and their subjective contents.

Limits of brain-computer interface. Case report.

Neurosurg Focus. 2006;20(5):e6

Authors: Bakay RA

Towards adaptive classification for BCI.

J Neural Eng. 2006 Mar;3(1):R13-23

Authors: Shenoy P, Krauledat M, Blankertz B, Rao RP, Müller KR

Non-stationarities are ubiquitous in EEG signals. They are especially apparent in the use of EEG-based brain-computer interfaces (BCIs): (a) in the differences between the initial calibration measurement and the online operation of a BCI, or (b) caused by changes in the subject's brain processes during an experiment (e.g. due to fatigue, change of task involvement, etc). In this paper, we quantify for the first time such systematic evidence of statistical differences in data recorded during offline and online sessions. Furthermore, we propose novel techniques of investigating and visualizing data distributions, which are particularly useful for the analysis of (non-)stationarities. Our study shows that the brain signals used for control can change substantially from the offline calibration sessions to online control, and also within a single session. In addition to this general characterization of the signals, we propose several adaptive classification schemes and study their performance on data recorded during online experiments. An encouraging result of our study is that surprisingly simple adaptive methods in combination with an offline feature selection scheme can significantly increase BCI performance.

Towards adaptive classification for BCI.

J Neural Eng. 2006 Mar;3(1):R13-23

Authors: Shenoy P, Krauledat M, Blankertz B, Rao RP, Müller KR

Non-stationarities are ubiquitous in EEG signals. They are especially apparent in the use of EEG-based brain-computer interfaces (BCIs): (a) in the differences between the initial calibration measurement and the online operation of a BCI, or (b) caused by changes in the subject's brain processes during an experiment (e.g. due to fatigue, change of task involvement, etc). In this paper, we quantify for the first time such systematic evidence of statistical differences in data recorded during offline and online sessions. Furthermore, we propose novel techniques of investigating and visualizing data distributions, which are particularly useful for the analysis of (non-)stationarities. Our study shows that the brain signals used for control can change substantially from the offline calibration sessions to online control, and also within a single session. In addition to this general characterization of the signals, we propose several adaptive classification schemes and study their performance on data recorded during online experiments. An encouraging result of our study is that surprisingly simple adaptive methods in combination with an offline feature selection scheme can significantly increase BCI performance.